Is network science useful for Roman studies? What’s so great about it, and what’s not? In January I gave a keynote talk on the topic at ‘Finding the limits of the Limes’. The talk was caught on film, so you can judge my arguments for yourself. It starts a bit negative but ends on a hopeful note (spoiler alert: I LOVE networks). Talk abstract below the video.
Do you have something to say about the way network methods are used in archaeology? Maybe you have some networky research lying around somewhere, begging to be presented. Or maybe you just need an excuse to come to Oslo and hang out with awesome academics! These are all good reasons to submit a paper to our networks session at CAA 2016 in Oslo (although I prefer the first two reasons to the third)! Together with Mereke van Garderen and Daniel Weidele, I will chair a session that aims to work towards best practice in archaeological network science. Since network methods are still very new in our discipline, there is a need to explore how they can be usefully and critically applied and developed to lead to insights that could not have been gained through any other approach. We believe there is a need to develop guidelines for best practice for archaeological network science that will help archaeologists explore the potential use of network science techniques for achieving their own research aims. Come join us and add your thoughts to the discussion!
Deadline call for papers: 25 October 2015
Session code: S16
Dates conference: 29 March – 2 April 2016
More CFP info: CAA website
For those interested in getting their hands dirty and learning how to use the awesome network science software Visone: we will also host a workshop at CAA Oslo!
Networking the past: Towards best practice in archaeological network science
The full diversity of network perspectives has only been introduced in our discipline relatively recently. As a result we are still in the long-term process of evaluating which theories and methods are available, the ‘fit’ between particular network perspectives and particular research questions, and how to apply these critically. How can network science usefully contribute to archaeological research by enabling archaeologists to answer important questions they could not have answered through other approaches? In what circumstances is the use of network science techniques appropriate? There is a need to address these questions by working towards guidelines to best practice in archaeological network science. This is a goal that should be achieved by a community of scholars in collaboration, drawing on the lessons learned from applying network science critically and creatively in a diversity of archaeological research contexts.
This session aims to build on the growing interest in and maturity of archaeological networks science to lay the foundations of guidelines for best practice in archaeological network science. It invites papers debating best practice in archaeological network science, addressing methodological and theoretical challenges posed by the archaeological application of network science, or presenting archaeological case studies applying network science techniques. It particularly welcomes papers presenting work in which the use of network science techniques was necessary and well theoretically motivated, and papers applying network science to exploring ‘oceans of data’.
We’ve all heard it before, people saying stuff like “everything’s connected to everything else”. Most often this phrase is used in the way Dirk Gently would use it in Douglas Adams’ novels. Dirk runs a ‘Holistic Detective Agency’ which means that what sets him apart from other more traditional detective agencies is that he solves crimes by figuring out how all people, things and events are related to each other. In practice, it means he has very few customers since this method can never be consistently applied within an acceptable time limit. In the end Dirk is always reliant on dumb luck to solve his cases, although he backs his decisions up with pseudo-scientific jargony nonsense.
Network scientists including archaeologists and historians rarely talk like Dirk Gently-style detectives (I believe anything between Sherlock Holmes and Poirot best describes the range of fictitious detective analogies for us applying networks to figure out the past, I’m on the Poirot side if you’re wondering). They are more specific in their description of how things are interrelated, thanks to their use of network science concepts: arcs, edges, actors, nodes, centrality, cliques, path length, etc. This sadly does not mean that these academics make themselves any more understandable to their audiences than Dirk Gently does, just because they happen to use jargon usefully and consistently.
When talking to network scientists you really need a dictionary at hand. I often catch myself assuming that many terms like, closeness centrality or ego-network, which have very specific formal definitions are widely known or at least intuitively understandable. It turns out this is definitely not the case: assuming jargon is widely known leads to bad communication, assuming it is intuitively understandable leads to no communication at best and bad science at worst. This issue is particularly problematic for us network scientists who try to contribute to archaeology or history, disciplines where network jargon is entirely unknown.
I am delighted that we are finally starting to overcome this issue. Archaeologists and historians: your networky dictionary has arrived! A while ago we published a special issue of Journal of Archaeological Method and Theory on archaeological network science. Because so many network science concepts were used over and over again in each paper of this issue, we decided to write a network science glossary. You can find this network science glossary on the Tutorials and Resources page of this blog as well as in the original paper. The glossary was written with an audience of archaeologists and historians in mind. It provides unambiguous non-technical explanations of key concepts as well as a number of examples. All of us who worked on it really hope this will help archaeologists and historians in particular to start critically engaging with all the amazing new work that’s appearing, and to produce some of it themselves.
It was first published as part of the introduction to our special issue of Journal of Archaeological Method and Theory on archaeological network science.
Please cite the glossary as follows:
Collar, A., Coward, F., Brughmans, T., & Mills, B. J. (2015). Networks in Archaeology: Phenomena, Abstraction, Representation. Journal of Archaeological Method and Theory, 22, 1–32. doi:10.1007/s10816-014-9235-6
I am super massively chuffed to announce that The Connected Past special issue of the Journal of Archaeological Method and theory is out now. It aims to provide examples of the critical and innovative use of network science in archaeology in order to inspire its more widespread use. What’s even better, the editorial is open access! And it’s accompanied by a glossary of network science techniques and concepts that we hope will prove to be a useful resource for archaeologists interested in network concepts.
My fellow editors Anna Collar, Fiona Coward, Barbara Mills and I are extremely grateful to all the authors of this special issue for their great contributions. You can read in the editorial the details of why we think these contributions are great. We would also like to thank the editors of Journal of Archaeological Method and Theory for offering us great support throughout the process, and to Springer for agreeing to make the editorial open access.
Original papers in this issue (Gotta read ’em all!):
Networks in Archaeology: Phenomena, Abstraction, Representation
by the editors Anna Collar, Fiona Coward, Tom Brughmans, and Barbara J. Mills
Are Social Networks Survival Networks? An Example from the Late Pre-Hispanic US Southwest
by Lewis Borck, Barbara J. Mills, Matthew A. Peeples, and Jeffery J. Clark
Understanding Inter-settlement Visibility in Iron Age and Roman Southern Spain with Exponential Random Graph Models for Visibility Networks
by Tom Brughmans, Simon Keay, and Graeme Earl
Inferring Ancestral Pueblo Social Networks from Simulation in the Central Mesa Verde
by Stefani A. Crabtree
Procurement and Distribution of Pre-Hispanic Mesoamerican Obsidian 900 BC–AD 1520: a Social Network Analysis
by Mark Golitko, and Gary M. Feinman
The Equifinality of Archaeological Networks: an Agent-Based Exploratory Lab Approach
by Shawn Graham, and Scott Weingart
Remotely Local: Ego-networks of Late Pre-colonial (AD 1000–1450) Saba, North-eastern Caribbean
by Angus A. A. Mol, Menno L. P. Hoogland, and Corinne L. Hofman
The Diffusion of Fired Bricks in Hellenistic Europe: A Similarity Network Analysis
by Per Östborn, and Henrik Gerding
Yes, there are similarities between my work and things that people actually want to spend time listening to. But you really have to look hard for them. I’ve given a lot of presentations over the last few years and noticed that one of the best ways of getting a difficult idea across is to use a movie analogy. I also learned I was very bad at preparing my presentation in time to think up movie analogies. So here’s a blog post to make up for it, inspired by a new paper I wrote that just came out in Journal of Archaeological Method and Theory. My work is a bit like Lord of the Rings and Mulan.
But not in the way you hope it is. There is nothing near as exciting in my research as the cavalry charges in both those movies, and I definitely never experienced a ‘montage’ moment that provided me in a ridiculously short timespan with the crazy skills needed to destroy the baddie.
What my work has in common with both Mulan and Lord of the Rings are fire signals. This video shows the memorable scene from Return of the King when the fire beacons in Gondor are lit, triggering the lighting of a chain of other beacons. The message is clear and reaches its target quickly: Gondor looks to Rohan for help.
The opening scene of Disney’s Mulan is similar: when the bad guys attack, beacons are lit all along the great wall of China to warn the Chinese people of the coming threat.
These scenes are not completely unthinkable fantasy scenarios, but are inspired by early communication systems that actually existed in the past. In times before telephones and telegraphs, signalling systems using fire, smoke, sound or light could have been used to spread messages over very long distances.
Archaeologists studying the Iron Age of Spain believe such a communication system might have existed in some regions. And it is easy to understand why. The settlement pattern in much of Spain during the Iron Age was dominated by large fortified urban settlements on hills, hence they are sometimes referred to as hillforts. These large urban settlements were surrounded by smaller rural settlements. The surrounding landscape could be visually controlled from many of these hillforts, and the hillforts were visually prominent features that could be seen from far away.
Now what scene of Lord of the Rings does this remind me of? That’s right: the Eye of Sauron perched on top of the massive tower of Barad Dur, scanning the surrounding landscape for his enemies, and to his followers acting as a visible reminder of who’s boss.
Many archaeologists believe these large urban settlements were located on hills on purpose, and not just because a hill was easier to defend but because of the views it offered: visually controlling the landscape, being visually prominent from its surroundings and acting as a good link in a fire signalling network.
And this is where networks come in! If archaeologists argue that settlements might have been located with visibility in mind for the three reasons mentioned here, then we should approach these statements as hypotheses that need testing. And we can do that in three ways using network science:
1) visualise the network of inter-visible settlements using our knowledge of the settlement pattern;
2) explore its structure to see whether it would function well as a communication network, or whether some settlements are more visually prominent;
3) simulate a process where places are settled so that a well-functioning communication network and/or a few more visually prominent settlements is established, and compare this simulated settlement pattern with the observed one.
This is what I do. The first approach uses network data representation and network layout algorithms to show a network of inter-visible settlements, and explore this pattern in a new way by extracting it from its geographical context and focusing just on its structure for a change. The second approach then uses exploratory network analysis techniques that tell us something more about individual nodes in the network and about the network as a whole (e.g. identify most visually prominent settlements, identify chains of inter-visible settlements). The third approach is in my eyes the most interesting one because it is totally new: using simulation models we generate millions of networks according to the process we hypothese might have taken place and we compare the simulated networks with the oberved ones using the same exploratory network analysis techniques as in the second approach.
This new approach to simulating our hypotheses about visibility networks is called exponential random graph modelling for visibility networks. A pair of papers just came out in which we introduce this method and apply it to Iron Age and Roman Southern Spain. The results are really interesting: there is no evidence for a well-functioning communication network, but there is definitely reason to believe that the pattern of visually prominent settlements that visually control surrounding rural settlements was purposefully established in the Iron Age. The importance of visibility as a factor determining settlement location then gradually decreases throughout Roman times.
Our recently published paper in Journal of Archaeological Method and Theory tells you the full story. The method is explained in detail in our paper in Journal of Archaeological Science. Both are available through my Academia.edu page or my bibliography on this blog. Enjoy!
As I mentioned before, I recently published a review of formal network methods in archaeology in Archaeological Review from Cambridge. I want to share the key problems I raise in this review here on my blog, because in many ways they are the outcomes of working with networks as an archaeologist the last six years. And yes, I encountered more problems than I was able to solve, which is a good thing because I do not want to be bored the next few years 🙂 In a series of four blog posts I draw on this review to introduce four groups of problems that archaeologists are faced with when using networks: method, data, space process. The full paper can be found on Academia. This first blog post in the series discusses methodological issues, enjoy 🙂
Like any other formal techniques in the archaeologist’s toolbox (e.g. GIS, radiocarbon dating, statistics), formal network techniques are methodological tools that work according to a set of known rules (the algorithms underlying them). These allow the analyst to answer certain questions (the network structural results of the algorithms), and have clear limitations (what the algorithms are not designed to answer). This means that their formal use is fundamentally limited by what they are designed to do, and that they can only be critically applied in an archaeological context when serving this particular purpose. In most cases, however, these formal network results are not the aim of one’s research; archaeologists do not use network methods just because they can. Instead one thinks through a networks perspective about the past interactions and systems one is actually interested in. This reveals an epistemological issue that all archaeological tools struggle with: there is a danger that formal networks are equated with the past networks we are trying to understand (Isaksen 2013; Knox et al. 2006; Riles 2001). In other cases, however, formal analysis is avoided altogether and concepts adopted from formal network methods are used to describe hypothetical past structures or processes (e.g. Malkin 2011). Although this sort of network thinking can lead to innovative hypotheses, it is not formal network analysis (see reviews of Malkin (2011) by Ruffini (2012) and Brughmans (2013)). However, such concepts adopted from formal network methods often have a very specific meaning to network analysts and are associated with data requirements in order to express them. Most crucially, when the concepts one uses to explain a hypothesis cannot be demonstrated through data (not even hypothetically through simulation), there is a real danger that these concepts become devalued since they are not more probable than any other hypotheses. Moreover, the interpretation of past social systems runs the risk of becoming mechanised when researchers adopt the typical interpretation of network concepts from the SNA or physics literature without validating their use with archaeological data or without modifying their interpretation to a particular archaeological research context. This criticism is addressed at the adoption of formal network concepts only. It should be clear that other theoretical concepts could well use a similar vocabulary whilst not sharing the same purpose or data requirements, in which case I would argue to refrain from using the same word to refer to different concepts or explicitly address the difference between these concepts in order to avoid confusion.
Although it is easy to claim that the rules underlying formal network techniques are known, it is less straightforward to assume that the traditional education of archaeologists allows them to decipher these algorithms. Archaeologists are not always sufficiently equipped to critique the mathematical underpinnings of network techniques, let alone to develop novel techniques tailor-made to address an archaeological question. For many archaeologists this means a real barrier or at least a very steep learning curve. Sadly, it also does not suffice to focus one’s efforts on the most common techniques or on learning graph theory. Like GIS, network analysis is not a single homogeneous method: it incorporates every formal technique that visualises or analyses the interactions between nodes (either hypothetical or observed), and it is only the particular nature of the network as a data type that holds these techniques together (Brandes et al. 2013). For this purpose it draws on graph theory, statistical and probability theory, algebraic models, but also agent-based modelling and GIS.
A thorough understanding of the technical underpinnings of particular network techniques is not an option; it is a prerequisite for a critical interpretation of the results. A good example of this is network visualisation. Many archaeologists consider the visualisation of networks as graphs a useful exploratory technique to understand the nature of their data, in particular when combined with geographical visualisations (e.g. Golitko et al. 2012). However, there are many different graph layout algorithms, and all of them are designed for a particular purpose: to communicate a certain structural feature most efficiently (Conway 2012; Freeman 2005). These days, user-friendly network analysis software is freely available and most of it includes a limited set of layouts, often not offering the option of modifying the impact of variables in the layout algorithms. Not understanding the underlying ‘graph drawing aesthetics’ or limiting one’s exploration to a single layout will result in routinized interpretations focusing on a limited set of the network’s structural features.
Archaeologists who consider the application of network methods to achieve their research aims must be able to identify and evaluate such issues. Multi-disciplinary engagement or even collaboration significantly aids this evaluation process.
Brandes, U., Robins, G., McCranie, A., & Wasserman, S. (2013). What is network science? Network Science, 1(01), 1–15. doi:10.1017/nws.2013.2
Brughmans, T. (2013). Review of I. Malkin 2011. A Small Greek World. Networks in the Ancient Mediterranean. The Classical Review, 63(01), 146–148. doi:10.1017/S0009840X12002776
Conway, S. (2012). A Cautionary Note on Data Inputs and Visual Outputs in Social Network Analysis. British Journal of Management. doi:10.1111/j.1467-8551.2012.00835.x
Freeman, L. C. (2005). Graphic techniques for exploring social network data. In P. J. Carrington, J. Scott, & S. Wasserman (Eds.), Models and methods in social network analysis (Vol. 5, pp. 248–268). Cambridge: Cambridge University Press. doi:10.3917/enje.005.0059
Golitko, M., Meierhoff, J., Feinman, G. M., & Williams, P. R. (2012). Complexities of collapse : the evidence of Maya obsidian as revealed by social network graphical analysis. Antiquity, 86, 507–523.
Isaksen, L. (2013). “O What A Tangled Web We Weave” – Towards a Practice That Does Not Deceive. In C. Knappett (Ed.), Network analysis in archaeology. New approaches to regional interaction (pp. 43–70). Oxford: Oxford University Press.
Knox, H., Savage, M., & Harvey, P. (2006). Social networks and the study of relations: networks as method, metaphor and form. Economy and Society, 35(1), 113–140. doi:10.1080/03085140500465899
Malkin, I. (2011). A small Greek world: networks in the Ancient Mediterranean. Oxford – New York: Oxford University Press.
Riles, A. (2001). The Network inside Out. Ann Arbor, MI: University of Michigan Press.
Ruffini, G. (2012). Review of Malkin, I. 2011 A Small Greek World: Networks in the Ancient Mediterranean. American Historical Review, 1643–1644.